Abstract
In this paper, we propose a parallel processing model based on systolic computing merged with concepts of evolutionary algorithms. The proposed model works over a Graphics Processing Unit using the structure of threads as cells that form a systolic mesh. Data passes through those cells, each one performing a simple computing operation. The systolic algorithm is implemented using NVIDIA’s compute unified device architecture. To investigate the behavior and performance of the proposed model we test it over a NP-complete problem. The study of systolic algorithms on GPU and the different versions of the proposal show that our canonical model is a competitive solver with efficacy and presents a good scalability behavior across different instance sizes.
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Acknowledgments
Pablo Vidal acknowledges continuous support from the University of Patagonia Austral. The work of Francisco Luna and Enrique Alba has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project). Francisco Luna also acknowledges support from TIN2011-28336.
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Communicated by G . Acampora.
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Vidal, P., Luna, F. & Alba, E. Systolic neighborhood search on graphics processing units. Soft Comput 18, 125–142 (2014). https://doi.org/10.1007/s00500-013-1041-7
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DOI: https://doi.org/10.1007/s00500-013-1041-7